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Type 'q()' to quit R. > library(nlme) > > options(digits = 6)# <==> less platform dependency in print() output > if(!dev.interactive(orNone=TRUE)) pdf("test_lme.pdf") > > fm1 <- lmList(Oxboys) > fm1 Call: Model: height ~ age | Subject Data: Oxboys Coefficients: (Intercept) age 10 130.262 3.72291 26 137.993 5.58878 25 139.210 4.02408 9 138.137 6.00906 2 142.858 5.44018 6 146.791 3.96317 7 146.128 4.99005 17 142.978 8.61178 16 147.545 4.54520 15 144.276 7.12426 8 148.293 6.46471 20 151.471 4.37447 1 148.120 7.17815 18 151.180 5.95779 5 151.429 6.24613 23 151.065 7.18512 11 150.047 8.50608 21 150.521 7.49779 3 155.651 4.77467 24 153.140 6.76470 22 154.567 8.08751 12 156.811 7.01547 13 156.071 8.49381 14 159.474 8.67089 19 164.576 9.06562 4 165.072 9.36056 Degrees of freedom: 234 total; 182 residual Residual standard error: 0.659888 > fm2 <- lme(fm1) > fm2 Linear mixed-effects model fit by REML Data: Oxboys Log-restricted-likelihood: -362.045 Fixed: height ~ age (Intercept) age 149.37175 6.52547 Random effects: Formula: ~age | Subject Structure: General positive-definite, Log-Cholesky parametrization StdDev Corr (Intercept) 8.081077 (Intr) age 1.680717 0.641 Residual 0.659889 Number of Observations: 234 Number of Groups: 26 > vc2 <- VarCorr(fm2) > stopifnot( + all.equal(fixef(fm2), c("(Intercept)" = 149.371753, + age = 6.52546866), tol=1e-8), + all.equal(as.numeric(vc2[,"StdDev"]), + c(8.081077, 1.680717, 0.659889), tol=4e-7)) > > # bug report from Arne.Mueller@sanofi-aventis.com > mod <- distance ~ age + Sex > fm3 <- lme(mod, Orthodont, random = ~ 1) > pm3 <- predict(fm3, Orthodont) > stopifnot(all.equal(mean(pm3), 24.023148148), + all.equal( sd(pm3), 2.4802195115), + all.equal(quantile(pm3), c("0%" = 17.0817792, "25%" = 22.3481813, + "50%" = 23.9271016, "75%" = 25.5740014, + "100%"= 30.8662241))) > > > ## bug report and fix from Dimitris Rizopoulos and Spencer Graves: > ## when 'returnObject = TRUE', do not stop() but give warning() on non-convergence: > tools::assertWarning( + fm1 <- lme(distance ~ age, data = Orthodont, + control = lmeControl(msMaxIter = 1, returnObject = TRUE)) + ) > > ## based on bug report on R-help > (p3.1 <- predict(fm3, Orthodont[1,])) M01 25.3924 attr(,"label") [1] "Predicted values (mm)" > # failed in 3.1-88 > stopifnot(all.equal(pm3[1], p3.1, + check.attributes=FALSE, tolerance = 1e-14)) > > ## Intervals failed in a patch proposal (Nov.2015): > (fm4 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)) Linear mixed-effects model fit by REML Data: Orthodont Log-restricted-likelihood: -221.318 Fixed: distance ~ age (Intercept) age 16.761111 0.660185 Random effects: Formula: ~age | Subject Structure: General positive-definite, Log-Cholesky parametrization StdDev Corr (Intercept) 2.327034 (Intr) age 0.226428 -0.609 Residual 1.310040 Number of Observations: 108 Number of Groups: 27 > i4 <- intervals(fm4) > ## from dput(signif(i4$reStruct$Subject, 8)) > ## R-devel 2016-01-11; 64-bit : > reSS <- data.frame(lower = c(0.9485605, 0.10250901, -0.93825047), + est. = c(2.3270341, 0.22642779, -0.60933286), + upper = c(5.7087424, 0.50014674, 0.29816857)) > ## R-devel 2016-01-11; 32-bit : > ## reSS <- data.frame(lower = c(0.94962127,0.10262181, -0.93804767), > ## est. = c(2.3270339, 0.22642779, -0.60933284), > ## upper = c(5.7023648, 0.49959695, 0.29662651)) > rownames(reSS) <- rownames(i4$reStruct$Subject) > sm4 <- summary(fm4) > stopifnot( + all.equal(fixef(fm4), + c("(Intercept)" = 16.761111, age = 0.66018519)), + identical(fixef(fm4), sm4$tTable[,"Value"]), + all.equal(sm4$tTable[,"Std.Error"], + c("(Intercept)" = 0.77524603, age = 0.071253264), tol=6e-8), + all.equal(i4$reStruct$Subject[,"est."], reSS[,"est."], tol= 1e-7) + ## (lower, upper) cannot be very accurate for these : ==> tol = *e-4 + ,## "interestingly" 32-bit values changed from 3.2.3 to R-devel(3.3.0): + all.equal(i4$reStruct$Subject[,c(1,3)], reSS[,c(1,3)], tol = .005) + , + all.equal(as.vector(i4$sigma), + ## lower est. upper + c(1.0849772, 1.3100397, 1.5817881), tol=8e-4) + , + all.equal(as.vector(i4$fixed), + as.vector(rbind(c(15.218322, 16.761111, 18.3039), + c(0.51838667, 0.66018519, 0.8019837))), + tol = 1e-6) + ) > > > ## wrong results from getData: > ss2 <- readRDS("ss2.rds") > m1 <- lme(PV1MATH ~ ESCS + Age +time , + random = ~ time|SCHOOLID, + data = ss2, + weights = varIdent(form=~1|time), + corr = corCompSymm(form=~1|SCHOOLID/StIDStd), + na.action = na.omit) > plot(m1, resid(.) ~ WEALTH) > > m2 <- lme(PV1MATH ~ ESCS + Age +time , + random = ~ time|SCHOOLID, + data = ss2, + weights = varIdent(form=~1|time), + corr = corCompSymm(form=~1|SCHOOLID/StIDStd), + na.action = na.omit) > plot(m2, resid(.) ~ WEALTH) > > > ## Variogram() failing in the case of 1-observation groups (PR#17192): > BW <- subset(BodyWeight, ! (Rat=="1" & Time > 1)) > if(interactive()) + print( xtabs(~ Rat + Time, data = BW) )# Rat '1' only at Time == 1 > fm2 <- lme(fixed = weight ~ Time * Diet, random = ~ 1 | Rat, data = BW) > Vfm2 <- Variogram(fm2, form = ~ Time | Rat) > stopifnot(is.data.frame(Vfm2), + identical(dim(Vfm2), c(19L, 3L)), + all.equal(unlist(Vfm2[10,]), c(variog = 1.08575384191148, + dist = 22, n.pairs = 15)) + ) > ## failed in nlme from 3.1-122 till 3.1-128 > > proc.time() user system elapsed 2.627 0.110 2.787